Experiment with Random Difficulty (results based on the confidence scale)
## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers based on BET
(pas nécessaire pour la mesure basée sur l’échelle de confiance)
{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #
{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #
{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #
{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #
{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #
Removing Outliers based on CONFIDENCE SCALE
## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 9"## [1] "Total number of outliers motor task: 0"## [1] "Total number of outliers perceptive task: 5"## [1] "Total number of outliers logical task: 7"Modeling difficulties
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 2016.5 2038.2 -1004.3 2008.5 1678
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1935 -0.7469 0.2908 0.7381 2.8784
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.559 0.7476
## Number of obs: 1682, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0580 0.1843 -5.74 9.48e-09 ***
## difficulty 3.0160 0.2115 14.26 < 2e-16 ***
## timeNorm -0.5213 0.1990 -2.62 0.00879 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.540
## timeNorm -0.572 -0.008## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1682 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.05422
## 1st Qu.:-0.44100
## Median :-0.11748
## Mean :-0.00241
## 3rd Qu.: 0.33077
## Max. : 1.65790
## [1] "Intercept: -1.06 9.5e-09 ***"
## [1] "Difficulty: 3.02 3.8e-46 ***"
## [1] "Time: -0.521 0.0088 **"
## [1] "R2 fixed: 0.17"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"## 0% 25% 50% 75% 100%
## -1.6579021 -0.3307656 0.1174780 0.4410031 1.0542161
## 0% 25% 50% 75% 100%
## -1.6579021 -0.3307656 0.1174780 0.4410031 1.0542161
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1157.7 1178.9 -574.8 1149.7 1475
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1882 -0.3711 0.1173 0.3487 6.1614
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7454 0.8634
## Number of obs: 1479, groups: IDjoueur, 51
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1584 0.2662 -11.864 <2e-16 ***
## difficulty 8.0854 0.4166 19.407 <2e-16 ***
## timeNorm -0.4665 0.2800 -1.666 0.0957 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.633
## timeNorm -0.507 -0.075## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge: degenerate Hessian with 1 negative
## eigenvalues## The result is correct only if all data used by the model has not changed since model was fitted.## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge: degenerate Hessian with 1 negative
## eigenvalues## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1479
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.664795
## 1st Qu.:-0.448260
## Median : 0.051307
## Mean :-0.001189
## 3rd Qu.: 0.429804
## Max. : 1.509537
## [1] "Intercept: -3.16 1.8e-32 ***"
## [1] "Difficulty: 8.09 6.7e-84 ***"
## [1] "Time: -0.467 0.096 ."
## [1] "R2 fixed: 0.29"
## [1] "R2 mixed: 0.45"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1200"## 0% 25% 50% 75% 100%
## -1.50953716 -0.42980436 -0.05130702 0.44825963 1.66479506
## 0% 25% 50% 75% 100%
## -1.50953716 -0.42980436 -0.05130702 0.44825963 1.66479506
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1444.5 1465.8 -718.2 1436.5 1533
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0357 -0.4980 -0.1017 0.5004 5.0622
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.57 1.253
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9054 0.2628 -7.251 4.14e-13 ***
## difficulty 5.7562 0.3198 18.001 < 2e-16 ***
## timeNorm -1.9355 0.2564 -7.550 4.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.497
## timeNorm -0.376 -0.233## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1537 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.8051717
## 1st Qu.:-0.7513212
## Median :-0.2064150
## Mean :-0.0003176
## 3rd Qu.: 0.7228639
## Max. : 3.1492300
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## 0% 25% 50% 75% 100%
## -3.1492300 -0.7228639 0.2064150 0.7513212 1.8051717
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3393, p-value = 0.1805
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1375478
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.86499, p-value = 0.387
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09516712
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.01388433Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.86388, p-value = 0.3877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08757052
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.29511, p-value = 0.7679
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03198946
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.06919576Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.16967, p-value = 0.8653
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02270513## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4333, p-value = 0.01496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3398094
##
## [1] "self.eff.on.level.s 0.34 0.015 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.06648267Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3157, p-value = 0.1883
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.127906
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.0165, p-value = 0.04374
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2093532
##
## [1] "risk.av.on.level.s 0.21 0.044 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1404273Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.1261, p-value = 0.2601
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1063448## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8814, p-value = 0.05991
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1899593
##
## [1] "age.on.level.s 0.19 0.06 ."## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1130316Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.3774, p-value = 0.01743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2593202
##
## [1] "sexe.on.level.m -0.26 0.017 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.0609, p-value = 0.9514
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.007100716
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04451521
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 227, p-value = 0.01687
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8465888 -0.1080105
## sample estimates:
## difference in location
## -0.4966452
##
## [1] "sexe.on.level.m.2 -0.5 0.017 * mean(A): 0.16 mean(B): -0.32"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 283, p-value = 0.96
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4684763 0.5456280
## sample estimates:
## difference in location
## 0.01412646
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.7753238 0.5708569
## sample estimates:
## difference in location
## -0.06017729CONFIDENCE SCALE APPROACH
For Bet approach, see the other file.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.079 44 0.0014 **
## 2: 0.09375 0.120 55 3.5e-05 ***
## 3: 0.15625 0.110 57 0.00021 ***
## 4: 0.21875 0.150 58 1e-06 ***
## 5: 0.28125 0.120 56 1.5e-05 ***
## 6: 0.34375 0.100 57 3.5e-05 ***
## 7: 0.40625 0.086 56 0.0098 **
## 8: 0.46875 0.015 57 0.41 :(
## 9: 0.53125 -0.010 56 0.39 :(
## 10: 0.59375 -0.062 58 0.003 **
## 11: 0.65625 -0.098 58 7.7e-05 ***
## 12: 0.71875 -0.120 57 5.3e-06 ***
## 13: 0.78125 -0.170 55 9e-08 ***
## 14: 0.84375 -0.210 52 3.2e-08 ***
## 15: 0.90625 -0.230 55 4.5e-10 ***
## 16: 0.96875 -0.170 55 1.7e-09 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.0014 **
## 2: 55 3.5e-05 ***
## 3: 57 0.00021 ***
## 4: 58 1e-06 ***
## 5: 56 1.5e-05 ***
## 6: 57 3.5e-05 ***
## 7: 56 0.0098 **
## 8: 57 0.41 :(
## 9: 56 0.39 :(
## 10: 58 0.003 **
## 11: 58 7.7e-05 ***
## 12: 57 5.3e-06 ***
## 13: 55 9e-08 ***
## 14: 52 3.2e-08 ***
## 15: 55 4.5e-10 ***
## 16: 55 1.7e-09 ***
## [1] 55.4
## [1] 3.4
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0690 23 0.13 :(
## 2: 0.09375 0.0560 28 0.3 :(
## 3: 0.15625 0.0940 38 0.12 :(
## 4: 0.21875 0.1100 37 0.0034 **
## 5: 0.28125 0.0940 36 0.019 *
## 6: 0.34375 0.0780 37 0.016 *
## 7: 0.40625 0.0640 36 0.033 *
## 8: 0.46875 0.0310 35 0.25 :(
## 9: 0.53125 -0.0062 34 0.66 :(
## 10: 0.59375 -0.0940 36 0.028 *
## 11: 0.65625 -0.1600 35 0.0015 **
## 12: 0.71875 -0.1900 34 6.8e-06 ***
## 13: 0.78125 -0.1900 31 0.00014 ***
## 14: 0.84375 -0.3100 16 0.0039 **
## 15: 0.90625 -0.2700 20 0.00017 ***
## 16: 0.96875 -0.1100 17 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 23 0.13 :(
## 2: 28 0.3 :(
## 3: 38 0.12 :(
## 4: 37 0.0034 **
## 5: 36 0.019 *
## 6: 37 0.016 *
## 7: 36 0.033 *
## 8: 35 0.25 :(
## 9: 34 0.66 :(
## 10: 36 0.028 *
## 11: 35 0.0015 **
## 12: 34 6.8e-06 ***
## 13: 31 0.00014 ***
## 14: 16 0.0039 **
## 15: 20 0.00017 ***
## 16: 17 0.056 .
## [1] 30.8
## [1] 7.57
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0540 31 0.01 *
## 2: 0.09375 0.1400 35 3e-04 ***
## 3: 0.15625 0.1100 36 0.0053 **
## 4: 0.21875 0.1500 37 0.00064 ***
## 5: 0.28125 0.1600 39 0.00052 ***
## 6: 0.34375 0.1200 37 0.011 *
## 7: 0.40625 0.0590 38 0.2 :(
## 8: 0.46875 -0.0170 36 0.72 :(
## 9: 0.53125 -0.0013 37 0.93 :(
## 10: 0.59375 -0.0600 34 0.11 :(
## 11: 0.65625 -0.1300 41 0.0014 **
## 12: 0.71875 -0.0690 38 0.024 *
## 13: 0.78125 -0.1400 39 2e-04 ***
## 14: 0.84375 -0.1800 37 1.3e-05 ***
## 15: 0.90625 -0.2100 36 1.1e-06 ***
## 16: 0.96875 -0.1200 30 4.2e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 31 0.01 *
## 2: 35 3e-04 ***
## 3: 36 0.0053 **
## 4: 37 0.00064 ***
## 5: 39 0.00052 ***
## 6: 37 0.011 *
## 7: 38 0.2 :(
## 8: 36 0.72 :(
## 9: 37 0.93 :(
## 10: 34 0.11 :(
## 11: 41 0.0014 **
## 12: 38 0.024 *
## 13: 39 2e-04 ***
## 14: 37 1.3e-05 ***
## 15: 36 1.1e-06 ***
## 16: 30 4.2e-05 ***
## [1] 36.3
## [1] 2.82
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 0.1600 11 0.12 :(
## 3: 0.15625 0.1300 14 0.016 *
## 4: 0.21875 0.0730 15 0.093 .
## 5: 0.28125 0.2200 13 0.011 *
## 6: 0.34375 0.1600 14 0.0021 **
## 7: 0.40625 0.1600 15 0.093 .
## 8: 0.46875 0.0310 18 0.085 .
## 9: 0.53125 -0.0560 17 0.07 .
## 10: 0.59375 -0.0940 19 0.45 :(
## 11: 0.65625 -0.0062 17 0.92 :(
## 12: 0.71875 -0.0690 19 0.06 .
## 13: 0.78125 -0.1600 19 0.0073 **
## 14: 0.84375 -0.2300 21 0.00032 ***
## 15: 0.90625 -0.2100 22 0.00018 ***
## 16: 0.96875 -0.3100 21 6.3e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 11 0.12 :(
## 2: 14 0.016 *
## 3: 15 0.093 .
## 4: 13 0.011 *
## 5: 14 0.0021 **
## 6: 15 0.093 .
## 7: 18 0.085 .
## 8: 17 0.07 .
## 9: 19 0.45 :(
## 10: 17 0.92 :(
## 11: 19 0.06 .
## 12: 19 0.0073 **
## 13: 21 0.00032 ***
## 14: 22 0.00018 ***
## 15: 21 6.3e-05 ***
## [1] 17
## [1] 3.25## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.094 9 0.63 :(
## 3: 0.15625 0.094 29 0.43 :(
## 4: 0.21875 0.069 41 0.037 *
## 5: 0.28125 0.094 47 0.018 *
## 6: 0.34375 0.110 50 0.013 *
## 7: 0.40625 0.069 50 0.074 .
## 8: 0.46875 0.040 51 0.036 *
## 9: 0.53125 0.035 54 0.15 :(
## 10: 0.59375 -0.029 53 0.41 :(
## 11: 0.65625 -0.081 54 0.0085 **
## 12: 0.71875 -0.069 54 0.0029 **
## 13: 0.78125 -0.110 45 0.00073 ***
## 14: 0.84375 -0.170 29 0.0045 **
## 15: 0.90625 -0.210 15 0.018 *
## 16: 0.96875 -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.63 :(
## 2: 29 0.43 :(
## 3: 41 0.037 *
## 4: 47 0.018 *
## 5: 50 0.013 *
## 6: 50 0.074 .
## 7: 51 0.036 *
## 8: 54 0.15 :(
## 9: 53 0.41 :(
## 10: 54 0.0085 **
## 11: 54 0.0029 **
## 12: 45 0.00073 ***
## 13: 29 0.0045 **
## 14: 15 0.018 *
## 15: 6 0.056 .
## [1] 39.1
## [1] 17.2## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.0940 9 0.63 :(
## 3: 0.15625 0.0940 26 0.4 :(
## 4: 0.21875 0.0790 27 0.073 .
## 5: 0.28125 0.1200 25 0.017 *
## 6: 0.34375 0.1100 27 0.0014 **
## 7: 0.40625 0.0690 26 0.032 *
## 8: 0.46875 0.0810 25 0.0095 **
## 9: 0.53125 0.0690 25 0.14 :(
## 10: 0.59375 0.0062 24 0.92 :(
## 11: 0.65625 -0.0400 25 0.33 :(
## 12: 0.71875 -0.0880 24 0.023 *
## 13: 0.78125 -0.0810 15 0.037 *
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.63 :(
## 2: 26 0.4 :(
## 3: 27 0.073 .
## 4: 25 0.017 *
## 5: 27 0.0014 **
## 6: 26 0.032 *
## 7: 25 0.0095 **
## 8: 25 0.14 :(
## 9: 24 0.92 :(
## 10: 25 0.33 :(
## 11: 24 0.023 *
## 12: 15 0.037 *
## [1] 23.2
## [1] 5.46## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 0.0670 14 0.29 :(
## 5: 0.28125 0.0690 21 0.31 :(
## 6: 0.34375 0.0460 22 0.67 :(
## 7: 0.40625 0.0190 22 0.92 :(
## 8: 0.46875 -0.0021 22 0.97 :(
## 9: 0.53125 0.0350 22 0.24 :(
## 10: 0.59375 -0.0770 22 0.2 :(
## 11: 0.65625 -0.1200 22 0.019 *
## 12: 0.71875 -0.0440 23 0.17 :(
## 13: 0.78125 -0.0810 22 0.079 .
## 14: 0.84375 -0.1800 21 0.024 *
## 15: 0.90625 -0.1900 7 0.15 :(
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 14 0.29 :(
## 2: 21 0.31 :(
## 3: 22 0.67 :(
## 4: 22 0.92 :(
## 5: 22 0.97 :(
## 6: 22 0.24 :(
## 7: 22 0.2 :(
## 8: 22 0.019 *
## 9: 23 0.17 :(
## 10: 22 0.079 .
## 11: 21 0.024 *
## 12: 7 0.15 :(
## [1] 20
## [1] 4.71## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 1 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 0.190 2 0.5 :(
## 8: 0.46875 NA 4 NA
## 9: 0.53125 -0.031 7 0.19 :(
## 10: 0.59375 -0.094 7 0.33 :(
## 11: 0.65625 -0.160 7 0.33 :(
## 12: 0.71875 -0.085 7 0.15 :(
## 13: 0.78125 -0.180 8 0.028 *
## 14: 0.84375 -0.160 8 0.1 :(
## 15: 0.90625 -0.210 8 0.055 .
## 16: 0.96875 -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 0.5 :(
## 2: 7 0.19 :(
## 3: 7 0.33 :(
## 4: 7 0.33 :(
## 5: 7 0.15 :(
## 6: 8 0.028 *
## 7: 8 0.1 :(
## 8: 8 0.055 .
## 9: 6 0.056 .
## [1] 6.67
## [1] 1.87## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0250 39 0.19 :(
## 2: 0.09375 0.0310 48 0.24 :(
## 3: 0.15625 0.0940 47 0.27 :(
## 4: 0.21875 0.0310 36 0.41 :(
## 5: 0.28125 0.0190 35 0.97 :(
## 6: 0.34375 -0.0190 29 0.74 :(
## 7: 0.40625 -0.0062 31 0.85 :(
## 8: 0.46875 -0.1200 31 0.031 *
## 9: 0.53125 -0.1800 28 0.004 **
## 10: 0.59375 -0.1900 34 0.0012 **
## 11: 0.65625 -0.1600 36 0.00091 ***
## 12: 0.71875 -0.2200 35 0.00034 ***
## 13: 0.78125 -0.2300 34 1.3e-05 ***
## 14: 0.84375 -0.2400 39 2.9e-05 ***
## 15: 0.90625 -0.2100 49 4.6e-08 ***
## 16: 0.96875 -0.0940 51 1.2e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 39 0.19 :(
## 2: 48 0.24 :(
## 3: 47 0.27 :(
## 4: 36 0.41 :(
## 5: 35 0.97 :(
## 6: 29 0.74 :(
## 7: 31 0.85 :(
## 8: 31 0.031 *
## 9: 28 0.004 **
## 10: 34 0.0012 **
## 11: 36 0.00091 ***
## 12: 35 0.00034 ***
## 13: 34 1.3e-05 ***
## 14: 39 2.9e-05 ***
## 15: 49 4.6e-08 ***
## 16: 51 1.2e-06 ***
## [1] 37.6
## [1] 7.34
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 8.4e-05 17 1 :(
## 2: 0.09375 -4.4e-02 16 0.36 :(
## 3: 0.15625 9.4e-02 15 0.79 :(
## 4: 0.21875 3.1e-02 9 0.63 :(
## 5: 0.28125 1.9e-02 12 0.91 :(
## 6: 0.34375 -1.7e-01 10 0.066 .
## 7: 0.40625 -1.6e-01 9 0.12 :(
## 8: 0.46875 -2.2e-01 13 0.017 *
## 9: 0.53125 -2.8e-01 9 0.057 .
## 10: 0.59375 -3.4e-01 12 0.0082 **
## 11: 0.65625 -2.8e-01 12 0.0024 **
## 12: 0.71875 -4.5e-01 11 0.0036 **
## 13: 0.78125 -2.8e-01 11 0.0086 **
## 14: 0.84375 -3.2e-01 13 0.0095 **
## 15: 0.90625 -2.0e-01 16 0.0017 **
## 16: 0.96875 -1.1e-01 17 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 17 1 :(
## 2: 16 0.36 :(
## 3: 15 0.79 :(
## 4: 9 0.63 :(
## 5: 12 0.91 :(
## 6: 10 0.066 .
## 7: 9 0.12 :(
## 8: 13 0.017 *
## 9: 9 0.057 .
## 10: 12 0.0082 **
## 11: 12 0.0024 **
## 12: 11 0.0036 **
## 13: 11 0.0086 **
## 14: 13 0.0095 **
## 15: 16 0.0017 **
## 16: 17 0.056 .
## [1] 12.6
## [1] 2.83
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.052 22 0.11 :(
## 2: 0.09375 0.031 24 0.29 :(
## 3: 0.15625 -0.023 22 0.51 :(
## 4: 0.21875 -0.019 19 0.98 :(
## 5: 0.28125 -0.012 16 0.9 :(
## 6: 0.34375 0.056 14 0.49 :(
## 7: 0.40625 0.019 17 0.7 :(
## 8: 0.46875 -0.069 14 0.61 :(
## 9: 0.53125 -0.110 14 0.16 :(
## 10: 0.59375 -0.069 15 0.38 :(
## 11: 0.65625 -0.160 18 0.059 .
## 12: 0.71875 -0.120 16 0.14 :(
## 13: 0.78125 -0.160 18 0.0067 **
## 14: 0.84375 -0.190 18 0.011 *
## 15: 0.90625 -0.180 23 0.00035 ***
## 16: 0.96875 -0.056 24 0.00086 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 22 0.11 :(
## 2: 24 0.29 :(
## 3: 22 0.51 :(
## 4: 19 0.98 :(
## 5: 16 0.9 :(
## 6: 14 0.49 :(
## 7: 17 0.7 :(
## 8: 14 0.61 :(
## 9: 14 0.16 :(
## 10: 15 0.38 :(
## 11: 18 0.059 .
## 12: 16 0.14 :(
## 13: 18 0.0067 **
## 14: 18 0.011 *
## 15: 23 0.00035 ***
## 16: 24 0.00086 ***
## [1] 18.4
## [1] 3.59
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.160 8 0.1 :(
## 3: 0.15625 0.220 10 0.024 *
## 4: 0.21875 0.089 8 0.29 :(
## 5: 0.28125 0.069 7 0.8 :(
## 6: 0.34375 0.090 5 0.18 :(
## 7: 0.40625 0.160 5 0.28 :(
## 8: 0.46875 NA 4 NA
## 9: 0.53125 -0.180 5 0.058 .
## 10: 0.59375 -0.140 7 0.02 *
## 11: 0.65625 -0.110 6 0.83 :(
## 12: 0.71875 -0.094 8 0.29 :(
## 13: 0.78125 -0.280 5 0.054 .
## 14: 0.84375 -0.190 8 0.057 .
## 15: 0.90625 -0.290 10 0.011 *
## 16: 0.96875 -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.1 :(
## 2: 10 0.024 *
## 3: 8 0.29 :(
## 4: 7 0.8 :(
## 5: 5 0.18 :(
## 6: 5 0.28 :(
## 7: 5 0.058 .
## 8: 7 0.02 *
## 9: 6 0.83 :(
## 10: 8 0.29 :(
## 11: 5 0.054 .
## 12: 8 0.057 .
## 13: 10 0.011 *
## 14: 10 0.0059 **
## [1] 7.29
## [1] 1.9## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.094 36 0.0044 **
## 2: 0.09375 0.160 41 3.1e-05 ***
## 3: 0.15625 0.170 42 8.4e-05 ***
## 4: 0.21875 0.260 44 3.2e-06 ***
## 5: 0.28125 0.220 36 0.00012 ***
## 6: 0.34375 0.160 40 5.4e-05 ***
## 7: 0.40625 0.094 44 0.0061 **
## 8: 0.46875 0.031 41 0.038 *
## 9: 0.53125 -0.031 38 0.5 :(
## 10: 0.59375 -0.044 42 0.41 :(
## 11: 0.65625 -0.056 40 0.46 :(
## 12: 0.71875 -0.069 39 0.0097 **
## 13: 0.78125 -0.150 44 0.00022 ***
## 14: 0.84375 -0.230 43 2.1e-07 ***
## 15: 0.90625 -0.260 42 4.7e-07 ***
## 16: 0.96875 -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 36 0.0044 **
## 2: 41 3.1e-05 ***
## 3: 42 8.4e-05 ***
## 4: 44 3.2e-06 ***
## 5: 36 0.00012 ***
## 6: 40 5.4e-05 ***
## 7: 44 0.0061 **
## 8: 41 0.038 *
## 9: 38 0.5 :(
## 10: 42 0.41 :(
## 11: 40 0.46 :(
## 12: 39 0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.150 13 0.035 *
## 2: 0.09375 0.160 13 0.025 *
## 3: 0.15625 0.130 12 0.077 .
## 4: 0.21875 0.230 12 0.016 *
## 5: 0.28125 -0.031 8 0.84 :(
## 6: 0.34375 0.160 13 0.15 :(
## 7: 0.40625 0.094 12 0.19 :(
## 8: 0.46875 0.031 11 0.16 :(
## 9: 0.53125 -0.031 11 0.22 :(
## 10: 0.59375 -0.094 11 0.16 :(
## 11: 0.65625 -0.160 7 0.2 :(
## 12: 0.71875 -0.220 9 0.011 *
## 13: 0.78125 -0.180 10 0.024 *
## 14: 0.84375 -0.340 7 0.031 *
## 15: 0.90625 -0.480 6 0.036 *
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 13 0.035 *
## 2: 13 0.025 *
## 3: 12 0.077 .
## 4: 12 0.016 *
## 5: 8 0.84 :(
## 6: 13 0.15 :(
## 7: 12 0.19 :(
## 8: 11 0.16 :(
## 9: 11 0.22 :(
## 10: 11 0.16 :(
## 11: 7 0.2 :(
## 12: 9 0.011 *
## 13: 10 0.024 *
## 14: 7 0.031 *
## 15: 6 0.036 *
## [1] 10.3
## [1] 2.38## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0520 22 0.11 :(
## 2: 0.09375 0.1800 25 0.00046 ***
## 3: 0.15625 0.2300 25 4e-04 ***
## 4: 0.21875 0.2800 24 0.00021 ***
## 5: 0.28125 0.2400 21 0.00036 ***
## 6: 0.34375 0.1800 17 0.0051 **
## 7: 0.40625 0.1200 21 0.039 *
## 8: 0.46875 0.0310 20 0.25 :(
## 9: 0.53125 0.0021 18 0.96 :(
## 10: 0.59375 -0.0260 20 0.75 :(
## 11: 0.65625 -0.1100 23 0.26 :(
## 12: 0.71875 -0.0190 20 0.42 :(
## 13: 0.78125 -0.1300 23 0.007 **
## 14: 0.84375 -0.2200 24 0.00015 ***
## 15: 0.90625 -0.2100 22 0.00081 ***
## 16: 0.96875 -0.3700 13 0.0019 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 22 0.11 :(
## 2: 25 0.00046 ***
## 3: 25 4e-04 ***
## 4: 24 0.00021 ***
## 5: 21 0.00036 ***
## 6: 17 0.0051 **
## 7: 21 0.039 *
## 8: 20 0.25 :(
## 9: 18 0.96 :(
## 10: 20 0.75 :(
## 11: 23 0.26 :(
## 12: 20 0.42 :(
## 13: 23 0.007 **
## 14: 24 0.00015 ***
## 15: 22 0.00081 ***
## 16: 13 0.0019 **
## [1] 21.1
## [1] 3.18
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 1 NA
## 2: 0.09375 NA 3 NA
## 3: 0.15625 0.0940 5 0.78 :(
## 4: 0.21875 0.0810 8 0.23 :(
## 5: 0.28125 0.3300 7 0.051 .
## 6: 0.34375 0.1600 10 0.0098 **
## 7: 0.40625 0.1800 11 0.17 :(
## 8: 0.46875 0.0810 10 0.31 :(
## 9: 0.53125 -0.0310 9 0.91 :(
## 10: 0.59375 0.0063 11 0.82 :(
## 11: 0.65625 0.0690 10 0.22 :(
## 12: 0.71875 -0.0690 10 0.36 :(
## 13: 0.78125 -0.0810 11 0.27 :(
## 14: 0.84375 -0.2300 12 0.0066 **
## 15: 0.90625 -0.2300 14 0.0038 **
## 16: 0.96875 -0.3400 14 0.0011 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.78 :(
## 2: 8 0.23 :(
## 3: 7 0.051 .
## 4: 10 0.0098 **
## 5: 11 0.17 :(
## 6: 10 0.31 :(
## 7: 9 0.91 :(
## 8: 11 0.82 :(
## 9: 10 0.22 :(
## 10: 10 0.36 :(
## 11: 11 0.27 :(
## 12: 12 0.0066 **
## 13: 14 0.0038 **
## 14: 14 0.0011 **
## [1] 10.1
## [1] 2.44## Warning: Removed 2 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71195 -0.16836 0.00376 0.17619 0.63833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.182297 0.019774 9.219 <2e-16 ***
## timeNorm 0.005893 0.020913 0.282 0.778
## obj.diff -0.375586 0.025858 -14.525 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05568596)
##
## Null deviance: 105.649 on 1681 degrees of freedom
## Residual deviance: 93.497 on 1679 degrees of freedom
## AIC: -79.355
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81749 -0.18021 -0.03534 0.21272 0.81986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04407 0.01847 2.386 0.0172 *
## timeNorm 0.05227 0.02452 2.132 0.0332 *
## obj.diff -0.27424 0.01908 -14.376 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06916715)
##
## Null deviance: 116.76 on 1478 degrees of freedom
## Residual deviance: 102.09 on 1476 degrees of freedom
## AIC: 251.47
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73430 -0.20594 -0.01949 0.19850 0.71398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21759 0.02001 10.88 <2e-16 ***
## timeNorm 0.05914 0.02495 2.37 0.0179 *
## obj.diff -0.53045 0.02119 -25.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06995631)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 107.31 on 1534 degrees of freedom
## AIC: 278.57
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5422414 0.6014885 -0.0544661582 116 0.038 *
## 2: 4.5 0.5367816 0.5712048 -0.0274664169 174 0.17 :(
## 3: 7.5 0.5155172 0.5413406 -0.0217582878 174 0.28 :(
## 4: 10.5 0.5413793 0.5404361 0.0102982443 174 0.62 :(
## 5: 13.5 0.5155172 0.5181081 -0.0005152110 174 0.97 :(
## 6: 16.5 0.5310345 0.5333167 -0.0007660154 174 0.97 :(
## 7: 19.5 0.5063218 0.5344527 -0.0290711237 174 0.12 :(
## 8: 22.5 0.4873563 0.4934513 -0.0053069701 174 0.8 :(
## 9: 25.5 0.4890805 0.4822968 0.0047959969 174 0.8 :(
## 10: 28.5 0.4741379 0.4548030 0.0173421720 174 0.4 :(
## time error.diff shapes
## 1: 1.5 -0.0544661582 24
## 2: 4.5 -0.0274664169 16
## 3: 7.5 -0.0217582878 16
## 4: 10.5 0.0102982443 16
## 5: 13.5 -0.0005152110 16
## 6: 16.5 -0.0007660154 16
## 7: 19.5 -0.0290711237 16
## 8: 22.5 -0.0053069701 16
## 9: 25.5 0.0047959969 16
## 10: 28.5 0.0173421720 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4676471 0.5966972 -0.13741579 102 2.6e-05 ***
## 2: 4.5 0.5071895 0.6251604 -0.10090751 153 1.4e-07 ***
## 3: 7.5 0.4653595 0.5391838 -0.07478623 153 0.00061 ***
## 4: 10.5 0.5163399 0.5905380 -0.07040539 153 3e-04 ***
## 5: 13.5 0.4673203 0.5737449 -0.09361413 153 4.6e-07 ***
## 6: 16.5 0.4196078 0.5182987 -0.10247746 153 5.9e-06 ***
## 7: 19.5 0.4790850 0.5454027 -0.05263313 153 0.0015 **
## 8: 22.5 0.4993464 0.5774717 -0.06609421 153 0.0013 **
## 9: 25.5 0.5483660 0.5978160 -0.03291924 153 0.054 .
## 10: 28.5 0.4993464 0.5710650 -0.06534653 153 0.0016 **
## time error.diff shapes
## 1: 1.5 -0.13741579 24
## 2: 4.5 -0.10090751 24
## 3: 7.5 -0.07478623 24
## 4: 10.5 -0.07040539 24
## 5: 13.5 -0.09361413 24
## 6: 16.5 -0.10247746 24
## 7: 19.5 -0.05263313 24
## 8: 22.5 -0.06609421 24
## 9: 25.5 -0.03291924 16
## 10: 28.5 -0.06534653 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4415094 0.6007697 -1.658770e-01 106 3.8e-06 ***
## 2: 4.5 0.5119497 0.6324837 -1.343840e-01 159 4.2e-06 ***
## 3: 7.5 0.5100629 0.5479813 -4.895619e-02 159 0.069 .
## 4: 10.5 0.5220126 0.5177334 2.196993e-03 159 0.93 :(
## 5: 13.5 0.5169811 0.5303606 -2.035258e-02 159 0.43 :(
## 6: 16.5 0.5100629 0.5026471 2.226322e-05 159 1 :(
## 7: 19.5 0.4584906 0.4514766 -3.401739e-03 159 0.87 :(
## 8: 22.5 0.4226415 0.4287566 -1.335901e-02 159 0.6 :(
## 9: 25.5 0.4584906 0.3964332 6.936761e-02 159 0.013 *
## 10: 28.5 0.4446541 0.3652666 6.326623e-02 159 0.012 *
## time error.diff shapes
## 1: 1.5 -1.658770e-01 24
## 2: 4.5 -1.343840e-01 24
## 3: 7.5 -4.895619e-02 16
## 4: 10.5 2.196993e-03 16
## 5: 13.5 -2.035258e-02 16
## 6: 16.5 2.226322e-05 16
## 7: 19.5 -3.401739e-03 16
## 8: 22.5 -1.335901e-02 16
## 9: 25.5 6.936761e-02 24
## 10: 28.5 6.326623e-02 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74494 -0.17245 -0.05919 0.23401 0.58312
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27589 0.03055 9.03 < 2e-16 ***
## timeNorm 0.08680 0.02952 2.94 0.00336 **
## obj.diff -0.61333 0.03125 -19.63 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06170749)
##
## Null deviance: 82.864 on 927 degrees of freedom
## Residual deviance: 57.079 on 925 degrees of freedom
## AIC: 53.74
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72188 -0.21303 -0.00161 0.20776 0.77339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14422 0.01739 8.294 <2e-16 ***
## timeNorm 0.05348 0.02125 2.517 0.0119 *
## obj.diff -0.36031 0.01960 -18.379 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07295822)
##
## Null deviance: 180.75 on 2116 degrees of freedom
## Residual deviance: 154.23 on 2114 degrees of freedom
## AIC: 470.76
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77262 -0.17089 -0.00174 0.20032 0.69533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12749 0.01662 7.67 2.93e-14 ***
## timeNorm 0.02037 0.02144 0.95 0.342
## obj.diff -0.34664 0.02125 -16.31 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05766731)
##
## Null deviance: 111.211 on 1652 degrees of freedom
## Residual deviance: 95.151 on 1650 degrees of freedom
## AIC: -20.108
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5359375 0.7914831 -0.25851324 64 1.3e-08 ***
## 2: 4.5 0.5708333 0.7798456 -0.22545680 96 8.2e-09 ***
## 3: 7.5 0.6052083 0.7544085 -0.16335208 96 4.3e-06 ***
## 4: 10.5 0.6291667 0.7221817 -0.09614468 96 0.003 **
## 5: 13.5 0.6260417 0.7613665 -0.16372251 96 7.4e-06 ***
## 6: 16.5 0.6281250 0.7309465 -0.11601158 96 0.00072 ***
## 7: 19.5 0.6125000 0.7089551 -0.10745208 96 0.00047 ***
## 8: 22.5 0.6072917 0.7286953 -0.12187730 96 0.00046 ***
## 9: 25.5 0.5927083 0.6849442 -0.08718941 96 0.011 *
## 10: 28.5 0.6125000 0.6539723 -0.03781422 96 0.25 :(
## time error.diff shapes
## 1: 1.5 -0.25851324 24
## 2: 4.5 -0.22545680 24
## 3: 7.5 -0.16335208 24
## 4: 10.5 -0.09614468 24
## 5: 13.5 -0.16372251 24
## 6: 16.5 -0.11601158 24
## 7: 19.5 -0.10745208 24
## 8: 22.5 -0.12187730 24
## 9: 25.5 -0.08718941 24
## 10: 28.5 -0.03781422 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5000000 0.5733496 -0.075648384 146 0.0046 **
## 2: 4.5 0.5520548 0.6435516 -0.084467252 219 6.9e-06 ***
## 3: 7.5 0.5118721 0.5237607 -0.017676852 219 0.38 :(
## 4: 10.5 0.5337900 0.5529666 -0.016029654 219 0.4 :(
## 5: 13.5 0.5347032 0.5429946 -0.009063747 219 0.64 :(
## 6: 16.5 0.4954338 0.5144682 -0.022588006 219 0.21 :(
## 7: 19.5 0.4926941 0.5151095 -0.026000186 219 0.16 :(
## 8: 22.5 0.4657534 0.4797826 -0.021965615 219 0.24 :(
## 9: 25.5 0.5200913 0.4857215 0.026431095 219 0.2 :(
## 10: 28.5 0.5000000 0.4710116 0.012688061 219 0.53 :(
## time error.diff shapes
## 1: 1.5 -0.075648384 24
## 2: 4.5 -0.084467252 24
## 3: 7.5 -0.017676852 16
## 4: 10.5 -0.016029654 16
## 5: 13.5 -0.009063747 16
## 6: 16.5 -0.022588006 16
## 7: 19.5 -0.026000186 16
## 8: 22.5 -0.021965615 16
## 9: 25.5 0.026431095 16
## 10: 28.5 0.012688061 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4394737 0.5259072 -0.0735740034 114 0.0088 **
## 2: 4.5 0.4485380 0.4666732 -0.0156461353 171 0.46 :(
## 3: 7.5 0.4198830 0.4484831 -0.0246561009 171 0.21 :(
## 4: 10.5 0.4614035 0.4460741 0.0204785496 171 0.31 :(
## 5: 13.5 0.3871345 0.4108429 -0.0182435888 171 0.41 :(
## 6: 16.5 0.4029240 0.4045515 -0.0002120625 171 0.99 :(
## 7: 19.5 0.3953216 0.3939035 -0.0027650308 171 0.89 :(
## 8: 22.5 0.3982456 0.3939114 0.0062870968 171 0.74 :(
## 9: 25.5 0.4157895 0.3876650 0.0277159604 171 0.13 :(
## 10: 28.5 0.3584795 0.3430010 0.0142558446 171 0.46 :(
## time error.diff shapes
## 1: 1.5 -0.0735740034 24
## 2: 4.5 -0.0156461353 16
## 3: 7.5 -0.0246561009 16
## 4: 10.5 0.0204785496 16
## 5: 13.5 -0.0182435888 16
## 6: 16.5 -0.0002120625 16
## 7: 19.5 -0.0027650308 16
## 8: 22.5 0.0062870968 16
## 9: 25.5 0.0277159604 16
## 10: 28.5 0.0142558446 16
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65081 -0.16600 -0.07689 0.21864 0.38438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29746 0.07745 3.841 0.000159 ***
## timeNorm 0.03979 0.04731 0.841 0.401279
## obj.diff -0.59239 0.08830 -6.709 1.52e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.03968561)
##
## Null deviance: 10.995 on 231 degrees of freedom
## Residual deviance: 9.088 on 229 degrees of freedom
## AIC: -85.242
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.6250000 0.8541813 -0.23116534 16 0.0013 **
## 2: 4.5 0.6375000 0.7984136 -0.16995810 24 0.0053 **
## 3: 7.5 0.6208333 0.7533950 -0.13245689 24 0.014 *
## 4: 10.5 0.6375000 0.7827081 -0.15599626 24 0.0079 **
## 5: 13.5 0.6250000 0.8239746 -0.20561865 24 4.4e-05 ***
## 6: 16.5 0.6375000 0.7813561 -0.15210779 24 0.027 *
## 7: 19.5 0.6541667 0.7252246 -0.07066985 24 0.14 :(
## 8: 22.5 0.6458333 0.7650575 -0.12329390 24 0.049 *
## 9: 25.5 0.6583333 0.7912822 -0.13403150 24 0.0072 **
## 10: 28.5 0.6166667 0.7394780 -0.11089775 24 0.042 *
## time error.diff shapes
## 1: 1.5 -0.23116534 24
## 2: 4.5 -0.16995810 24
## 3: 7.5 -0.13245689 24
## 4: 10.5 -0.15599626 24
## 5: 13.5 -0.20561865 24
## 6: 16.5 -0.15210779 24
## 7: 19.5 -0.07066985 16
## 8: 22.5 -0.12329390 24
## 9: 25.5 -0.13403150 24
## 10: 28.5 -0.11089775 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7128 -0.1799 0.0080 0.1979 0.6542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.160601 0.040886 3.928 9.46e-05 ***
## timeNorm 0.003705 0.038216 0.097 0.923
## obj.diff -0.347965 0.054087 -6.433 2.39e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07289063)
##
## Null deviance: 51.554 on 666 degrees of freedom
## Residual deviance: 48.399 on 664 degrees of freedom
## AIC: 151.12
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5413043 0.6313063 -0.080414494 46 0.073 .
## 2: 4.5 0.5652174 0.6292224 -0.057371089 69 0.099 .
## 3: 7.5 0.5420290 0.5592216 -0.011452588 69 0.74 :(
## 4: 10.5 0.5463768 0.5820863 -0.022036607 69 0.57 :(
## 5: 13.5 0.5550725 0.5449914 0.012093320 69 0.72 :(
## 6: 16.5 0.5478261 0.5622564 -0.019457251 69 0.62 :(
## 7: 19.5 0.4942029 0.5766338 -0.086681165 69 0.0077 **
## 8: 22.5 0.4681159 0.5121072 -0.050443461 69 0.17 :(
## 9: 25.5 0.5014493 0.4988278 -0.003887111 69 0.93 :(
## 10: 28.5 0.5014493 0.4985043 -0.010272074 69 0.7 :(
## time error.diff shapes
## 1: 1.5 -0.080414494 16
## 2: 4.5 -0.057371089 16
## 3: 7.5 -0.011452588 16
## 4: 10.5 -0.022036607 16
## 5: 13.5 0.012093320 16
## 6: 16.5 -0.019457251 16
## 7: 19.5 -0.086681165 24
## 8: 22.5 -0.050443461 16
## 9: 25.5 -0.003887111 16
## 10: 28.5 -0.010272074 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61019 -0.15879 0.00778 0.17071 0.53696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13601 0.02536 5.362 1.08e-07 ***
## timeNorm 0.01638 0.02758 0.594 0.553
## obj.diff -0.23883 0.03902 -6.121 1.47e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0443567)
##
## Null deviance: 36.420 on 782 degrees of freedom
## Residual deviance: 34.598 on 780 degrees of freedom
## AIC: -212.38
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5185185 0.5012163 0.020987089 54 0.54 :(
## 2: 4.5 0.4827160 0.4544613 0.033464592 81 0.19 :(
## 3: 7.5 0.4617284 0.4632778 0.001764609 81 0.95 :(
## 4: 10.5 0.5086420 0.4331719 0.087371264 81 0.0012 **
## 5: 13.5 0.4493827 0.4045805 0.051873659 81 0.053 .
## 6: 16.5 0.4851852 0.4351713 0.052762168 81 0.042 *
## 7: 19.5 0.4728395 0.4419957 0.028567591 81 0.25 :(
## 8: 22.5 0.4567901 0.3970833 0.064184236 81 0.019 *
## 9: 25.5 0.4283951 0.3766636 0.052786293 81 0.026 *
## 10: 28.5 0.4086420 0.3332279 0.073327560 81 0.0036 **
## time error.diff shapes
## 1: 1.5 0.020987089 16
## 2: 4.5 0.033464592 16
## 3: 7.5 0.001764609 16
## 4: 10.5 0.087371264 24
## 5: 13.5 0.051873659 16
## 6: 16.5 0.052762168 24
## 7: 19.5 0.028567591 16
## 8: 22.5 0.064184236 24
## 9: 25.5 0.052786293 24
## 10: 28.5 0.073327560 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74322 -0.20483 -0.03202 0.20676 0.62415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20999 0.04489 4.678 4.47e-06 ***
## timeNorm 0.04941 0.05280 0.936 0.35
## obj.diff -0.51260 0.04492 -11.413 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06276797)
##
## Null deviance: 26.320 on 289 degrees of freedom
## Residual deviance: 18.014 on 287 degrees of freedom
## AIC: 25.159
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5200000 0.6508420 -0.15316874 20 0.076 .
## 2: 4.5 0.5233333 0.6781967 -0.15793428 30 0.011 *
## 3: 7.5 0.5600000 0.7239238 -0.17383717 30 0.004 **
## 4: 10.5 0.6166667 0.7072602 -0.09559555 30 0.1 :(
## 5: 13.5 0.6300000 0.7376158 -0.09754771 30 0.045 *
## 6: 16.5 0.5033333 0.6329179 -0.17388810 30 0.022 *
## 7: 19.5 0.5666667 0.6721874 -0.14418598 30 0.064 .
## 8: 22.5 0.6766667 0.7257057 -0.04280707 30 0.54 :(
## 9: 25.5 0.5200000 0.6342124 -0.10335371 30 0.088 .
## 10: 28.5 0.5400000 0.6167904 -0.06210791 30 0.32 :(
## time error.diff shapes
## 1: 1.5 -0.15316874 16
## 2: 4.5 -0.15793428 24
## 3: 7.5 -0.17383717 24
## 4: 10.5 -0.09559555 16
## 5: 13.5 -0.09754771 24
## 6: 16.5 -0.17388810 24
## 7: 19.5 -0.14418598 16
## 8: 22.5 -0.04280707 16
## 9: 25.5 -0.10335371 16
## 10: 28.5 -0.06210791 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74943 -0.17271 0.02931 0.16064 0.81301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03489 0.02687 1.298 0.1945
## timeNorm 0.06011 0.03525 1.705 0.0886 .
## obj.diff -0.20242 0.02776 -7.291 8.41e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0672497)
##
## Null deviance: 50.406 on 695 degrees of freedom
## Residual deviance: 46.604 on 693 degrees of freedom
## AIC: 101.41
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5291667 0.6078474 -0.086598352 48 0.04 *
## 2: 4.5 0.5944444 0.6846758 -0.062208797 72 0.0014 **
## 3: 7.5 0.4833333 0.5068404 -0.039510796 72 0.25 :(
## 4: 10.5 0.5347222 0.6049774 -0.057714866 72 0.048 *
## 5: 13.5 0.4944444 0.5748430 -0.068004375 72 0.0076 **
## 6: 16.5 0.4694444 0.5248235 -0.047269189 72 0.13 :(
## 7: 19.5 0.5166667 0.5374851 -0.005537462 72 0.81 :(
## 8: 22.5 0.5069444 0.5711729 -0.055336688 72 0.044 *
## 9: 25.5 0.6111111 0.6185741 -0.004538522 72 0.8 :(
## 10: 28.5 0.5680556 0.5944001 -0.028304406 72 0.22 :(
## time error.diff shapes
## 1: 1.5 -0.086598352 24
## 2: 4.5 -0.062208797 24
## 3: 7.5 -0.039510796 16
## 4: 10.5 -0.057714866 24
## 5: 13.5 -0.068004375 24
## 6: 16.5 -0.047269189 16
## 7: 19.5 -0.005537462 16
## 8: 22.5 -0.055336688 24
## 9: 25.5 -0.004538522 16
## 10: 28.5 -0.028304406 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70974 -0.14355 -0.04811 0.22707 0.79366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.006679 0.029713 0.225 0.822
## timeNorm 0.038072 0.041830 0.910 0.363
## obj.diff -0.291208 0.031993 -9.102 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06709016)
##
## Null deviance: 38.482 on 492 degrees of freedom
## Residual deviance: 32.874 on 490 degrees of freedom
## AIC: 72.117
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3500000 0.5491058 -0.21304774 34 9e-04 ***
## 2: 4.5 0.3745098 0.5099408 -0.11854584 51 0.00081 ***
## 3: 7.5 0.3843137 0.4761745 -0.07531510 51 0.021 *
## 4: 10.5 0.4313725 0.5014928 -0.07835851 51 0.014 *
## 5: 13.5 0.3333333 0.4758000 -0.13976005 51 0.00025 ***
## 6: 16.5 0.3000000 0.4416642 -0.13413221 51 9.5e-05 ***
## 7: 19.5 0.3745098 0.4820011 -0.08242696 51 0.00086 ***
## 8: 22.5 0.3843137 0.4991676 -0.09930056 51 0.012 *
## 9: 25.5 0.4764706 0.5471006 -0.04133532 51 0.12 :(
## 10: 28.5 0.3784314 0.5112239 -0.11440884 51 5e-04 ***
## time error.diff shapes
## 1: 1.5 -0.21304774 24
## 2: 4.5 -0.11854584 24
## 3: 7.5 -0.07531510 24
## 4: 10.5 -0.07835851 24
## 5: 13.5 -0.13976005 24
## 6: 16.5 -0.13413221 24
## 7: 19.5 -0.08242696 24
## 8: 22.5 -0.09930056 24
## 9: 25.5 -0.04133532 16
## 10: 28.5 -0.11440884 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7180 -0.1523 -0.0701 0.2646 0.5316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.36438 0.05390 6.761 4.83e-11 ***
## timeNorm 0.11540 0.04908 2.351 0.0192 *
## obj.diff -0.74698 0.05334 -14.004 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07166686)
##
## Null deviance: 45.424 on 405 degrees of freedom
## Residual deviance: 28.882 on 403 degrees of freedom
## AIC: 87.062
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4964286 0.8561134 -0.36716892 28 7.5e-08 ***
## 2: 4.5 0.5666667 0.8418417 -0.29004109 42 9e-07 ***
## 3: 7.5 0.6285714 0.7767624 -0.16850164 42 0.0071 **
## 4: 10.5 0.6333333 0.6982535 -0.07050893 42 0.21 :(
## 5: 13.5 0.6238095 0.7425551 -0.16270974 42 0.023 *
## 6: 16.5 0.7119048 0.7721614 -0.07008744 42 0.23 :(
## 7: 19.5 0.6214286 0.7259209 -0.11110926 42 0.023 *
## 8: 22.5 0.5357143 0.7100524 -0.19151750 42 0.0023 **
## 9: 25.5 0.6071429 0.6604165 -0.04212140 42 0.55 :(
## 10: 28.5 0.6619048 0.6316703 0.02540027 42 0.62 :(
## time error.diff shapes
## 1: 1.5 -0.36716892 24
## 2: 4.5 -0.29004109 24
## 3: 7.5 -0.16850164 24
## 4: 10.5 -0.07050893 16
## 5: 13.5 -0.16270974 24
## 6: 16.5 -0.07008744 16
## 7: 19.5 -0.11110926 24
## 8: 22.5 -0.19151750 24
## 9: 25.5 -0.04212140 16
## 10: 28.5 0.02540027 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64743 -0.21112 -0.01143 0.18810 0.69300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.24374 0.02866 8.503 <2e-16 ***
## timeNorm 0.04799 0.03619 1.326 0.185
## obj.diff -0.53789 0.03229 -16.657 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07228863)
##
## Null deviance: 76.556 on 753 degrees of freedom
## Residual deviance: 54.289 on 751 degrees of freedom
## AIC: 163.93
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4365385 0.4902360 -0.056484920 52 0.23 :(
## 2: 4.5 0.5012821 0.6182667 -0.127211343 78 0.0017 **
## 3: 7.5 0.5115385 0.5080102 -0.009983459 78 0.85 :(
## 4: 10.5 0.5217949 0.4791968 0.031134779 78 0.36 :(
## 5: 13.5 0.5538462 0.5118296 0.042413783 78 0.27 :(
## 6: 16.5 0.4730769 0.4626353 -0.001909564 78 0.96 :(
## 7: 19.5 0.4692308 0.4400298 0.018917033 78 0.6 :(
## 8: 22.5 0.4256410 0.3668275 0.061870825 78 0.2 :(
## 9: 25.5 0.4525641 0.3514944 0.103712580 78 0.0095 **
## 10: 28.5 0.4358974 0.3327941 0.093319755 78 0.026 *
## time error.diff shapes
## 1: 1.5 -0.056484920 16
## 2: 4.5 -0.127211343 24
## 3: 7.5 -0.009983459 16
## 4: 10.5 0.031134779 16
## 5: 13.5 0.042413783 16
## 6: 16.5 -0.001909564 16
## 7: 19.5 0.018917033 16
## 8: 22.5 0.061870825 16
## 9: 25.5 0.103712580 24
## 10: 28.5 0.093319755 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.59428 -0.20393 0.00244 0.19029 0.63266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20091 0.03687 5.449 9.2e-08 ***
## timeNorm -0.02360 0.04793 -0.492 0.623
## obj.diff -0.50212 0.04973 -10.097 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05609687)
##
## Null deviance: 27.59 on 376 degrees of freedom
## Residual deviance: 20.98 on 374 degrees of freedom
## AIC: -11.147
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3923077 0.5468516 -0.14292222 26 0.043 *
## 2: 4.5 0.4743590 0.4354555 0.03036380 39 0.64 :(
## 3: 7.5 0.3794872 0.3815438 -0.01099869 39 0.79 :(
## 4: 10.5 0.4025641 0.4004002 0.01597914 39 0.85 :(
## 5: 13.5 0.3282051 0.3389055 -0.01793324 39 0.63 :(
## 6: 16.5 0.3666667 0.2924247 0.07494671 39 0.11 :(
## 7: 19.5 0.2615385 0.1788149 0.07887150 39 0.14 :(
## 8: 22.5 0.2948718 0.2496809 0.01582368 39 0.58 :(
## 9: 25.5 0.3102564 0.2020211 0.11168377 39 0.06 .
## 10: 28.5 0.2282051 0.1433153 0.06983217 39 0.26 :(
## time error.diff shapes
## 1: 1.5 -0.14292222 24
## 2: 4.5 0.03036380 16
## 3: 7.5 -0.01099869 16
## 4: 10.5 0.01597914 16
## 5: 13.5 -0.01793324 16
## 6: 16.5 0.07494671 16
## 7: 19.5 0.07887150 16
## 8: 22.5 0.01582368 16
## 9: 25.5 0.11168377 16
## 10: 28.5 0.06983217 16
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77757 -0.18933 0.01113 0.18652 0.79661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05597 0.01134 4.938 8.68e-07 ***
## est.confidence.norm -0.14072 0.02000 -7.035 2.90e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06108697)
##
## Null deviance: 105.65 on 1681 degrees of freedom
## Residual deviance: 102.63 on 1680 degrees of freedom
## AIC: 75.35
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.90565 -0.16725 0.02824 0.12302 0.97241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.07952 0.01503 -5.291 1.4e-07 ***
## est.confidence.norm -0.01092 0.02562 -0.426 0.67
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07904426)
##
## Null deviance: 116.76 on 1478 degrees of freedom
## Residual deviance: 116.75 on 1477 degrees of freedom
## AIC: 447.89
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.95702 -0.21545 -0.02456 0.22257 0.95697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05462 0.01698 3.217 0.00132 **
## est.confidence.norm -0.12831 0.02840 -4.518 6.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1006412)
##
## Null deviance: 156.54 on 1536 degrees of freedom
## Residual deviance: 154.48 on 1535 degrees of freedom
## AIC: 836.57
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1065
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5059 -0.6519 0.0074 0.6098 3.5085
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01085 0.1042
## Residual 0.07098 0.2664
## Number of obs: 4698, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.708e-03 1.617e-02 9.100e+01 -0.353 0.724894
## est.confidence.norm -5.257e-02 1.521e-02 4.623e+03 -3.457 0.000551 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.472
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -642.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1060 -0.6742 -0.0493 0.7179 3.1837
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02811 0.1677
## Residual 0.03562 0.1887
## Number of obs: 1682, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01775 0.02581 91.60000 -0.687 0.494
## est.confidence.norm 0.01288 0.02640 1544.90000 0.488 0.626
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.491
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 316.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2130 -0.6554 0.0639 0.5572 3.8015
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01206 0.1098
## Residual 0.06770 0.2602
## Number of obs: 1479, groups: IDjoueur, 51
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11754 0.02438 137.90000 -4.822 3.71e-06 ***
## est.confidence.norm 0.06327 0.03446 724.00000 1.836 0.0667 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.724
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 685.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9615 -0.6605 -0.0590 0.6527 3.2759
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01927 0.1388
## Residual 0.08483 0.2913
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04730 0.02869 130.10000 -1.649 0.1016
## est.confidence.norm 0.06558 0.03824 912.70000 1.715 0.0867 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.701## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1065
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5059 -0.6519 0.0074 0.6098 3.5085
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01085 0.1042
## Residual 0.07098 0.2664
## Number of obs: 4698, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.708e-03 1.617e-02 9.100e+01 -0.353 0.724894
## est.confidence.norm -5.257e-02 1.521e-02 4.623e+03 -3.457 0.000551 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.472
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -642.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1060 -0.6742 -0.0493 0.7179 3.1837
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.02811 0.1677
## Residual 0.03562 0.1887
## Number of obs: 1682, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01775 0.02581 91.60000 -0.687 0.494
## est.confidence.norm 0.01288 0.02640 1544.90000 0.488 0.626
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.491
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 316.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2130 -0.6554 0.0639 0.5572 3.8015
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01206 0.1098
## Residual 0.06770 0.2602
## Number of obs: 1479, groups: IDjoueur, 51
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11754 0.02438 137.90000 -4.822 3.71e-06 ***
## est.confidence.norm 0.06327 0.03446 724.00000 1.836 0.0667 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.724
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 685.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9615 -0.6605 -0.0590 0.6527 3.2759
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01927 0.1388
## Residual 0.08483 0.2913
## Number of obs: 1537, groups: IDjoueur, 53
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.04730 0.02869 130.10000 -1.649 0.1016
## est.confidence.norm 0.06558 0.03824 912.70000 1.715 0.0867 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.701Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.72809, p-value = 0.4666
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.04442222Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.6498, p-value = 0.008054
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1596456
##
## [1] "pbg.on.error -0.16 0.0081 **"In game level and subjective difficulty error
## [1] "niveau.group.on.error 0.021 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.0686, p-value = 0.03858
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1101489
##
## [1] "niveau.group.on.error 0.11 0.039 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2812, p-value = 0.2001
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1155475
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.90157, p-value = 0.3673
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08705882
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.4881, p-value = 0.1367
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1407837Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.9671, p-value = 0.003006
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.192626
##
## [1] "sexe.on.error 0.19 0.003 **"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2674, p-value = 0.205
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1382439
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.0706, p-value = 0.0384
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2414243
##
## [1] "sexe.on.error.s 0.24 0.038 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8362, p-value = 0.06633
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2098574
##
## [1] "sexe.on.error.l 0.21 0.066 ."
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3528, p-value = 0.02979
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.002911783 0.081656441
## sample estimates:
## difference in location
## 0.04249642
##
## [1] "sexe.on.error.2 0.042 0.03 * mean(A): -0.052 mean(B): -0.0014"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 429, p-value = 0.3397
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.02960968 0.10329903
## sample estimates:
## difference in location
## 0.03552981
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 351, p-value = 0.1538
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.01626915 0.12422612
## sample estimates:
## difference in location
## 0.05025256
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 397, p-value = 0.1744
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.01728579 0.12101991
## sample estimates:
## difference in location
## 0.04580971Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.77238, p-value = 0.4399
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04460327
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.46154, p-value = 0.6444
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04486755
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.4833, p-value = 0.6289
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05017556
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.37011, p-value = 0.7113
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.03771476Self efficacy and subjective difficulty error
## Warning: Removed 80 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.971, p-value = 0.002968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2333842
##
## [1] "self.eff.on.error -0.23 0.003 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 29 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.3385, p-value = 0.1807
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1791183## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 24 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.7201, p-value = 0.08542
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2402101
##
## [1] "self.eff.on.error -0.24 0.085 ."## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 27 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0192, p-value = 0.04346
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2880916
##
## [1] "self.eff.on.error -0.29 0.043 *"